Field
This disclosure is generally related to location check-in applications. More specifically, this disclosure is related to a method and system for ensuring a threshold level of check-in data stream quality for applications.
Related Art
Location is a vital piece of information for ubiquitous computing that enhances our everyday lives, since it is required for computers to be context-aware. For example, a smartphone should be able to detect a shop nearby and remind the user to perform a task at that shop. Moreover, computing systems can use location traces of users to infer certain characteristics of the user, such as a user's eating preferences, or what the user would like to do on the weekends. A location trace is also called a check-in stream. The location trace or check-in stream is a series of check-ins performed by a user at various locations.
Location information collected over a period of time can help a user detect serendipitous meetings with friends or like-minded individuals. One way to track locations of users of ubiquitous devices such as smartphones or tablets is to monitor the location of the user using global positioning system (GPS) or other beacon-based location systems. However, this method has a number of disadvantages. GPS puts an enormous load on the energy-constrained battery operated devices. GPS points cannot be readily translated into semantic locations like home, office, or coffee shop, especially when the user is located in cluttered environments. Lastly, there are a number of privacy concerns with GPS tracking.
Location Based Social Networks (LBSNs) such as Foursquare and Facebook Places can offer an alternative solution. With LBSNs, the user voluntarily checks in the user's location with semantic address tags like home or the name of a coffee shop or shopping mall. Since a user checks in the exact semantic location/address, the data association problem linked with GPS traces is taken care of. Moreover, the voluntary user check-in eliminates a number of privacy concerns found with GPS tracking. Alternatively, financial data, such as credit card or debit card purchases, using digital portals like Google Wallet, PayPal or Yodlee API, can offer a form of check-in information that ties the user to a place and even activity. Similarly, check-ins like gym visits, workplace sign-ons or even promotional check-ins at coffee shops or amusement parks using near field communication-enabled devices can also offer data for tracking a user's behavioral data.
However, the primary disadvantage of check-in based location tracking is the inherently sporadic nature of a user checking in their current location and activity. A user may forget to check-in his/her position or even intentionally choose not to report the current position/activity. Sparse check-in-based location traces can lead to poor performance of applications using collaborative filtering and location predictors that rely on the location traces.